You will get a personalized recommender system with API and MongoDB integration
Project details
I will build a personalized recommendation system tailored to your data. Whether you’re recommending movies, books, products, or any other items, I will deliver an end-to-end machine learning pipeline that includes data preprocessing, model training, evaluation, and deployment via a REST API.
With experience in Python, FastAPI, Scikit-learn, and recommendation techniques (content-based, collaborative filtering), I ensure production-ready code, clear documentation, and client-focused results.
With experience in Python, FastAPI, Scikit-learn, and recommendation techniques (content-based, collaborative filtering), I ensure production-ready code, clear documentation, and client-focused results.
AI Development Type
Recommendation SystemAI Tools
MLflowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$200
|
Standard
$350
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 4 days | 6 days | 8 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $150
Additional Revision
+$20
Docker Containerization & CI/CD Setup
(+ 2 Days)
+$100Frequently asked questions
About Manuel Alejandro
Data Scientist | ML Engineer | Recommender Systems | NLP | MLOps
Mexico City, Mexico - 9:26 pm local time
I specialize in building end-to-end ML systems for real-world applications such as fraud detection, personalized recommendations, time series forecasting, and text classification.
My projects are designed for production, featuring modular code, API deployment with FastAPI, Docker containerization, automated pipelines, and clear documentation.
I work with Python, Scikit-learn, XGBoost, FastAPI, Streamlit, SQL (PostgreSQL), and also use R for data exploration.
Whether you need a custom ML model, an automated ETL pipeline, or a visual dashboard for your data, I can deliver clean, scalable solutions.
Steps for completing your project
After purchasing the project, send requirements so Manuel Alejandro can start the project.
Delivery time starts when Manuel Alejandro receives requirements from you.
Manuel Alejandro works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Preprocessing
I review your dataset structure, clean and format it, and prepare it for modeling.
Model Development & Evaluation
I train one or more recommendation models (content-based or collaborative filtering) and evaluate their performance using standard metrics.